Analysis of repeated low-dose challenge studies.

Abstract

Preclinical evaluation of candidate human immunodeficiency virus (HIV) vaccines entails challenge studies whereby non-human primates such as macaques are vaccinated with either an active or control vaccine and then challenged (exposed) with a simian-version of HIV. Repeated low-dose challenge (RLC) studies in which each macaque is challenged multiple times (either until infection or some maximum number of challenges is reached) are becoming more common in an effort to mimic natural exposure to HIV in humans. Statistical methods typically employed for the testing for a vaccine effect in RLC studies include a modified version of Fisher's exact test as well as large sample approaches such as the usual log-rank test. Unfortunately, these methods are not guaranteed to provide a valid test for the effect of vaccination. On the other hand, valid tests for vaccine effect such as the exact log-rank test may not be easy to implement using software available to many researchers. This paper details which statistical approaches are appropriate for the analysis of RLC studies, and how to implement these methods easily in SAS or R.

Empirical type 1 error rate and power for the exact LRT {solid line (full=black, partial=gray)}, score test (dot/dashed line), and Wald test {dashed line (full=black, partial=gray)} as a function of relative risk (ϕ) for p0 = 0.5 probability of infection per challenge when not vaccinated, n = 10 macaques (five per arm), and maximum challenges per macaque. Scenario 1 includes no heterogeneity or immunity, Scenario 2 includes heterogeneity, and Scenario 3 includes immunity. The dotted horizontal line corresponds to the nominal significance level α = 0.05.